The selection of relevant attributes to be grouped can take inspiration from the works in the field of feature selection [1]} [2]}. In particular, the methods proposed in a dimensional reduction goal seem to reach our scope. Indeed, these methods have to automatically detect interactions between attributes for reducing a potential high dimensionality in a dataset. Thus, two main approaches, feature extraction (mainly the principal component analysis) and filter methods (which measure the relevance of features by their correlations) can be considered.
The fact that the principal component analysis (PCA) and the filter methods rely only on information provided by a dataset (independent of the model used in analysis) is a great advantage for our work, in contrast with techniques such as SVM-RFE [3]} or FS-P [4]}, based on a specific model.
Indeed, different predictive models can classify differently the same instance. Thus, an explanation on this instance can be different from one model to another, and cannot depend on a selection of influence attributes made by a unique predictive model, such as SVM.
The PCA is a largely recognized method to provide new features from sets of correlated attributes.
The Correlation-based feature selection (CFS) methods [5]} are promising candidates. In particular, the use of a multicollinearity measure by a variance inflation factor (VIF), can provide sets of attributes having linear correlations between them. This avoids calculating collinearity between pairs of attributes, using Pearson's measure, for example.
However, the VIF measure is unable to compute non-linear correlations, on the contrary of the Spearman correlation factor. Even if this factor only works between pairs of attributes, the capacity to detect non-linear correlations makes it a good candidate.
